[CHORES][VLLM]

mkdocs.yml

@@ -80,6 +80,7 @@ nav:
       - OpenAI: "swarms/models/openai.md"
       - Zephyr: "swarms/models/zephyr.md"
       - BioGPT: "swarms/models/biogpt.md"
+      - vLLM: "swarms/models/vllm.md"
       - MPT7B: "swarms/models/mpt.md"
       - Mistral: "swarms/models/mistral.md"
     - MultiModal:

playground/demos/optimize_llm_stack/vllm.py

@@ -7,7 +7,7 @@ custom_vllm = vLLM(
     trust_remote_code=True,
     revision="abc123",
     temperature=0.7,
-    top_p=0.8
+    top_p=0.8,
 )
 
 # Generate text with custom configuration

playground/demos/optimize_llm_stack/weaviate.py

@@ -12,3 +12,20 @@ weaviate_client = WeaviateClient(
     additional_config=None,  # You can pass additional configuration here
 )
 
+weaviate_client.create_collection(
+    name="my_collection",
+    properties=[
+        {"name": "property1", "dataType": ["string"]},
+        {"name": "property2", "dataType": ["int"]},
+    ],
+    vectorizer_config=None,  # Optional vectorizer configuration
+)
+
+weaviate_client.add(
+    collection_name="my_collection",
+    properties={"property1": "value1", "property2": 42},
+)
+
+results = weaviate_client.query(
+    collection_name="people", query="name:John", limit=5
+)

swarms/memory/__init__.py

@@ -1,7 +1,8 @@
-# from swarms.memory.pinecone import PineconeVector
-# from swarms.memory.base import BaseVectorStore
-# from swarms.memory.pg import PgVectorVectorStore
-from swarms.memory.weaviate import WeaviateClient
+try:
+    from swarms.memory.weaviate import WeaviateClient
+except ImportError:
+    pass
+
 from swarms.memory.base_vectordb import VectorDatabase
 
 __all__ = [

swarms/memory/base.py

@@ -1,130 +0,0 @@
-from abc import ABC, abstractmethod
-from concurrent import futures
-from dataclasses import dataclass
-from typing import Optional, Any
-from attr import define, field, Factory
-from swarms.utils.execute_futures import execute_futures_dict
-from griptape.artifacts import TextArtifact
-
-
-@define
-class BaseVectorStore(ABC):
-    DEFAULT_QUERY_COUNT = 5
-
-    @dataclass
-    class QueryResult:
-        id: str
-        vector: list[float]
-        score: float
-        meta: Optional[dict] = None
-        namespace: Optional[str] = None
-
-    @dataclass
-    class Entry:
-        id: str
-        vector: list[float]
-        meta: Optional[dict] = None
-        namespace: Optional[str] = None
-
-    embedding_driver: Any
-    futures_executor: futures.Executor = field(
-        default=Factory(lambda: futures.ThreadPoolExecutor()),
-        kw_only=True,
-    )
-
-    def upsert_text_artifacts(
-        self,
-        artifacts: dict[str, list[TextArtifact]],
-        meta: Optional[dict] = None,
-        **kwargs,
-    ) -> None:
-        execute_futures_dict(
-            {
-                namespace: self.futures_executor.submit(
-                    self.upsert_text_artifact,
-                    a,
-                    namespace,
-                    meta,
-                    **kwargs,
-                )
-                for namespace, artifact_list in artifacts.items()
-                for a in artifact_list
-            }
-        )
-
-    def upsert_text_artifact(
-        self,
-        artifact: TextArtifact,
-        namespace: Optional[str] = None,
-        meta: Optional[dict] = None,
-        **kwargs,
-    ) -> str:
-        if not meta:
-            meta = {}
-
-        meta["artifact"] = artifact.to_json()
-
-        if artifact.embedding:
-            vector = artifact.embedding
-        else:
-            vector = artifact.generate_embedding(
-                self.embedding_driver
-            )
-
-        return self.upsert_vector(
-            vector,
-            vector_id=artifact.id,
-            namespace=namespace,
-            meta=meta,
-            **kwargs,
-        )
-
-    def upsert_text(
-        self,
-        string: str,
-        vector_id: Optional[str] = None,
-        namespace: Optional[str] = None,
-        meta: Optional[dict] = None,
-        **kwargs,
-    ) -> str:
-        return self.upsert_vector(
-            self.embedding_driver.embed_string(string),
-            vector_id=vector_id,
-            namespace=namespace,
-            meta=meta if meta else {},
-            **kwargs,
-        )
-
-    @abstractmethod
-    def upsert_vector(
-        self,
-        vector: list[float],
-        vector_id: Optional[str] = None,
-        namespace: Optional[str] = None,
-        meta: Optional[dict] = None,
-        **kwargs,
-    ) -> str:
-        ...
-
-    @abstractmethod
-    def load_entry(
-        self, vector_id: str, namespace: Optional[str] = None
-    ) -> Entry:
-        ...
-
-    @abstractmethod
-    def load_entries(
-        self, namespace: Optional[str] = None
-    ) -> list[Entry]:
-        ...
-
-    @abstractmethod
-    def query(
-        self,
-        query: str,
-        count: Optional[int] = None,
-        namespace: Optional[str] = None,
-        include_vectors: bool = False,
-        **kwargs,
-    ) -> list[QueryResult]:
-        ...

swarms/memory/chroma_db.py

@@ -1,12 +1,14 @@
-import os
-from termcolor import colored
 import logging
+import os
 from typing import Dict, List, Optional
+
 import chromadb
 import tiktoken as tiktoken
 from chromadb.config import Settings
 from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from dotenv import load_dotenv
+from termcolor import colored
+
 from swarms.utils.token_count_tiktoken import limit_tokens_from_string
 
 load_dotenv()

swarms/models/__init__.py

@@ -8,7 +8,12 @@ from swarms.models.openai_models import (
     AzureOpenAI,
     OpenAIChat,
 )  # noqa: E402
-from swarms.models.vllm import vLLM  # noqa: E402
+
+try:
+    from swarms.models.vllm import vLLM  # noqa: E402
+except ImportError:
+    pass
+
 # from swarms.models.zephyr import Zephyr  # noqa: E402
 from swarms.models.biogpt import BioGPT  # noqa: E402
 from swarms.models.huggingface import HuggingfaceLLM  # noqa: E402
@@ -59,4 +64,5 @@ __all__ = [
     # "Dalle3",
     # "DistilWhisperModel",
     "GPT4VisionAPI",
+    "vLLM",
 ]